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All Journal Berumpun: international journal of social, politics and humanities Building of Informatics, Technology and Science Pioneer: Journal of Language and Literature JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Education Research Journal Of Human And Education (JAHE) Formosa Journal of Science and Technology (FJST) Journal of Artificial Intelligence and Digital Business APPA : Jurnal Pengabdian Kepada Masyarakat
Amalia, Resti
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Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences Other

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Journal : Formosa Journal of Science and Technology (FJST)

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Classification of Autoimmune Diseases Using the K-Nearest Neighbors Algorithm Amalia, Resti; Zaidan, Ahmad Faiz; Ramadhan, Syahrul; Septian, Farhan; Aqsha, Ananta Mikail; Rosyani, Perani
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13443

Abstract

Autoimmune diseases occur when the immune system attacks the body’s own tissues, causing serious complications and overlapping symptoms that challenge early detection. This study reviews the use of the K-Nearest Neighbors (K-NN) algorithm for classifying autoimmune diseases through a systematic literature review of five articles. Compared to methods like Genetic Algorithms, Support Vector Machines (SVM), and Single Layer Perceptrons (SLP), K-NN shows high accuracy when optimal parameters and neighbor counts are used. However, challenges include sensitivity to imbalanced data and high computational demands for large datasets. Combining K-NN with optimization techniques, such as Genetic Algorithms, enhances accuracy and stability. The study concludes that K-NN is effective for classifying autoimmune diseases, especially with hybrid approaches, and recommends further research with larger datasets.
Co-Authors Aqsha, Ananta Mikail Ariksa, Jeri Asshadath, Farhan Awaliyatunniza, Putri Biagi, Muhammad Micko Bintang, Muhammad Iqbal Ginanjar, Angga Harpada, Rifky Herza, Herza Ikasari, Ines Ikasari, Ines Hediani Ines Heidiani Ikasari Lubis, Donal Fernado Muammar Kurniawan Ningsih, Eka Setya Perani Rosyani Piyana, Ika Pratama, Lucky Whyzly Rahmi Eka Putri, Rahmi Eka Ramadhan, Miftah Fathu Rani, Dina Sayyidina Rulian, Marsandy Saprudin Saprudin Saprudin Saprudin, Saprudin Septian, Farhan Syahrul R, Syahrul Taufiq, M. Aries Tri Arie Bowo Wijaya, Mochammad Iqbal Suyudi Zaidan, Ahmad Faiz